Health
Breast cancer care: AI tools could help doctors personalize care
Engineers at the University of Waterloo have developed an artificial intelligence (AI) tool to help oncologists decide whether breast cancer patients should receive chemotherapy before surgery.
The new algorithm may help some patients avoid the severe side effects of chemotherapy, making it easier for those who would benefit from treatment to receive chemotherapy. It is part of his Cancer-Net information-sharing initiative, which is open source and led by Dr. Alexander Wong, professor of system design engineering at the university.
“Determining the appropriate treatment for a particular breast cancer patient is currently very difficult, and it is important to avoid unnecessary side effects from using treatments that are unlikely to provide real benefit to that patient. is. Media release issued on Tuesday.
“AI systems that can help predict whether a patient will respond well to a given treatment will give doctors the tools they need to prescribe the best personalized treatment for their patients to improve their recovery and survival. provide to.”
Barriers to treatment
Breast cancer is the second most common type of cancer in women in Canada and the United States, accounting for over 25% of new cancer cases in all women.
Cancer specialists often do surgery to remove cancer tissue and stop the breast cancer from growing, but some nonmetastatic breast cancers are inoperable.
To solve this problem, more and more experts are turning to a treatment called neoadjuvant chemotherapy. This treatment works by shrinking large tumors to make surgery possible, or at least easier. It can also reduce the need for major surgery such as a mastectomy.
However, not everyone is an ideal candidate for neoadjuvant chemotherapy.
“Neoadjuvant chemotherapy is expensive, time-consuming, and not only exposes patients to radiation, but can cause other significant side effects, such as decreased fertility,” says graduate student Amy Tai. The authors, led by wrote on paper Published in November 2022.
Alexander Wong leads the open source Cancer-Net initiative, is Director of the Vision and Imaging Lab at the University of Waterloo, and is the Canadian Research Commissioner for Artificial Intelligence and Medical Imaging. (University of Waterloo)
Tai, Wong, and their co-authors set out to create an algorithmic tool to help physicians determine which patients would benefit from treatment and which would not. The team used images of breast cancer produced by a new MRI technique they developed called synthetic correlated diffusion imaging (CDI) to train the AI algorithm.
Armed with knowledge gleaned from CDI images of older breast cancer cases and information about their outcomes, AI tools have learned to accurately predict whether a patient will benefit from preoperative chemotherapy based on CDI images. bottom.
The team’s next step is to validate the data by testing the technology in a comprehensive study with a larger patient group.
“I am very optimistic about this technology because deep learning AI has the potential to see and discover patterns that are relevant to whether a patient will benefit from a particular treatment.”
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